import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


__a    :	Dict         =    {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				XLNetConfig.from_json_file(lowercase				)

				__lowercase					     =				finetuning_task.lower() if finetuning_task is not None else ''''''
				if finetuning_task in GLUE_TASKS_NUM_LABELS:
								print(F"Building PyTorch XLNetForSequenceClassification model from configuration: {config}"				)
								__lowercase					     =				finetuning_task
								__lowercase					     =				GLUE_TASKS_NUM_LABELS[finetuning_task]
								__lowercase					     =				XLNetForSequenceClassification(lowercase				)
				elif "squad" in finetuning_task:
								__lowercase					     =				finetuning_task
								__lowercase					     =				XLNetForQuestionAnswering(lowercase				)
				else:
								__lowercase					     =				XLNetLMHeadModel(lowercase				)

				# Load weights from tf checkpoint
				load_tf_weights_in_xlnet(lowercase				,     lowercase				,     lowercase				)

				# Save pytorch-model
				__lowercase					     =				os.path.join(lowercase				,     lowercase				)
				__lowercase					     =				os.path.join(lowercase				,     lowercase				)
				print(F"Save PyTorch model to {os.path.abspath(lowercase				)}"				)
				torch.save(model.state_dict()				,     lowercase				)
				print(F"Save configuration file to {os.path.abspath(lowercase				)}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(config.to_json_string()				)


if __name__ == "__main__":
		__a    :	List[str]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--xlnet_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained XLNet model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
		)
		parser.add_argument(
		    """--finetuning_task""",
		    default=None,
		    type=str,
		    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
		)
		__a    :	Optional[int]         =    parser.parse_args()
		print(args)

		convert_xlnet_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
		)
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)